The storage of structured data in relational databases that may be queried using structured query language (SQL) has been progressively developed since the 1970's. For example, large vendors may use relational databases to store customer profiles and order history. While such databases can be quite vast, they work efficiently only when the scope of the data retrieved is relatively narrow such as customer profiles.
In contrast to conventional relational databases, “knowledge engines” that may query corresponding knowledge databases over a vast scope of topics have been developed. But the complexity for such knowledge engines becomes overwhelming due to the broad scope of entities and attributes that may be queried. For example, one user may wish to ask “When was William Jefferson Clinton born?” Another user may ask the same knowledge engine, “What was the population of Barrow, Ak. in 2012?” There is thus a need in the art for knowledge engines and corresponding knowledge databases that can efficiently accommodate the complexity and scope of the structured data being queried.
In addition, the knowledge databases are becoming massive and unwieldy due to the sheer amount of structured data they are tasked to store. There is thus a need in the art for knowledge databases having an organization that enables efficient storage yet accommodates speedy and accurate searches by a corresponding knowledge engine.